A modified YOLOv5 helmet detection algorithm based on Swin Transformer

Abstract

For the current stage of helmet detection in complex environments with low accuracy, missed detection and not easy to manage wearing, this paper proposes a YOLOv5 face helmet detection algorithm based on Swin Transformer improvement from the overall semantics of the image. In this paper, experiments are conducted using a self-built dataset to further enhance the performance of the model and improve the accuracy of face helmet detection through Mosaic data enhancement, label smoothing processing, adaptive weighted features combined with Wconcat module and the application of C3TR and C3STR modules to fuse multi-scale information, enhance the feature extraction capability of the network, and improve the generalization and robustness of the model with a self-built dataset . Experiments show that the improved YOLOv5 face helmet detection algorithm mAP based on Swin Transformer improves 5.7% compared with Faster RCNN, 6.1% compared with YOLOV3, 5.3% compared with YOLOV4, and 1.6% compared with the original algorithm. It performs well in helmet face detection tasks in complex environments, achieving real-time detection and higher accuracy, while reducing missed detections.